{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09e3213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset = pd.read_csv(r\"C:\\Users\\AOC\\Desktop\\作业二需求及数据\\Wholesale customers data.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "774d13e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "504480d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1088589c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70833271, 0.53987376, 0.42274083, 0.01196489, 0.14950522,\n",
       "        0.07480852],\n",
       "       [0.44219826, 0.61470384, 0.59953989, 0.11040858, 0.20634248,\n",
       "        0.11128583],\n",
       "       [0.39655169, 0.5497918 , 0.47963217, 0.15011913, 0.2194673 ,\n",
       "        0.48961931],\n",
       "       ...,\n",
       "       [0.36446153, 0.38846468, 0.7585445 , 0.01096068, 0.37223685,\n",
       "        0.04682745],\n",
       "       [0.93773743, 0.1805304 , 0.20340427, 0.09459392, 0.01531   ,\n",
       "        0.19365326],\n",
       "       [0.67229603, 0.40960124, 0.60547651, 0.01567967, 0.11506466,\n",
       "        0.01254374]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#在此使用Normalizer---使用方法同MinMaxScaler、StandardScaler\n",
    "from sklearn.preprocessing import Normalizer\n",
    "nor = Normalizer().fit_transform(dataset.iloc[:,2:])\n",
    "nor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb384221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.metrics import silhouette_samples\n",
    "import matplotlib.pyplot as plt\n",
    "score=[]\n",
    "for i in range(2,7):\n",
    "    cluster= KMeans(n_clusters=i, random_state=0).fit(nor)\n",
    "    score.append(silhouette_score(nor,cluster.labels_))\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(range(2,7),score)\n",
    "plt.axvline(pd.DataFrame(score).idxmax()[0]+2,ls=':')\n",
    "plt.show\n",
    "\n",
    "print(pd.DataFrame(score).idxmax()[0]+2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0b08d279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 2,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0,\n",
       "       0, 1, 2, 1, 2, 0, 2, 1, 0, 1, 2, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       2, 1, 1, 2, 0, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 0, 1, 1, 1, 1, 0, 1, 2, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 2, 1, 0, 1, 1, 2, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 2, 1, 1, 0, 2, 0, 1, 2, 0, 0, 0, 1, 1, 1, 0, 1, 1, 2, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 2, 2, 1, 1, 1, 2, 0, 2, 2, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 2, 2, 0, 2, 1, 0, 1, 1, 2, 1, 1, 1, 2, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 2, 0, 0, 1, 0, 2, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 2, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 2, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 2, 2, 0, 1, 2,\n",
       "       1, 0, 1, 0, 2, 1, 1, 2, 2, 2, 0, 0, 0, 0, 2, 0, 0, 1, 1, 0, 2, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 1, 2, 0, 1, 0, 1, 1, 1, 2, 0, 0, 1, 1, 1,\n",
       "       0, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 2, 2, 0, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 1, 2, 0, 1, 0, 1, 0, 2, 1, 0, 1, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster= KMeans(n_clusters=3, random_state=420).fit(nor)\n",
    "silhouette_score(nor,cluster.labels_)\n",
    "y_pred = cluster.predict(nor)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "654d999f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18c45abc400>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie([pd.DataFrame(nor[y_pred==0]).count()[0]/dataset.shape[0],pd.DataFrame(nor[y_pred==1]).count()[0]/dataset.shape[0],pd.DataFrame(nor[y_pred==2]).count()[0]/dataset.shape[0]], autopct='%0.2f%%')\n",
    "plt.legend(['0','1',\"2\"],fontsize=10,loc='center',ncol=1,\n",
    "          bbox_to_anchor=(0.9,0.9)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "692b9e2d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
